Gloria Choi studies how the brain learns to recognize olfactory stimuli and associate them with appropriate behavioral responses. Using mouse models, Choi works to anatomically and functionally map the circuitry that connects sensory representations to specific behavioral outcomes, and also investigates how learning transforms this circuitry and how the brain maintains behavioral plasticity so that responses to stimuli are context-dependent. Choi joined the MIT faculty in 2013 as an assistant professor in the Department of Brain and Cognitive Sciences and a McGovern Investigator. She received her bachelor’s degree from the University of California at Berkeley, and her PhD from Caltech, where she studied with David Anderson. She was a postdoc in the laboratory of Richard Axel at Columbia University.

Christopher Cummins, Henry Dreyfus Professor of Energy

Christopher Cummins develops synthetic methods that create novel molecular substances. He focuses on the activation of small, stable molecules through transition metal systems, particularly with respect to synthetic nitrogen fixation, carbon dioxide reduction, and phosphorous utilization. His work has important implications in a variety of fields, ranging from solar energy technology to exploratory synthesis investigations involving interstellar molecules generated using molecular precursors. After carrying out undergraduate research in chemistry at Middlebury College and Stanford University, Cummins graduated in 1989 from Cornell University. He completed his doctoral work under the direction of MIT Professor Richard Schrock in 1993, and joined the MIT Department of Chemistry faculty that same year.

James DiCarlo, Peter De Florez Professor of Neuroscience

James DiCarlo, head of the Department of Brain and Cognitive Sciences and investigator at the McGovern Institute, uses a combination of large-scale neurophysiology, brain imaging, optogenetic methods, and high-throughput computational simulations to understand the neuronal mechanisms and cortical computations that underlie the brain’s remarkable visual capabilities. He aims to use this understanding to inspire and develop new machine-vision systems, to provide a basis for new neural prosthetics (brain-machine interfaces) to restore or augment lost senses, and to provide a foundation upon which the community can understand how high-level visual representation is altered in human conditions such as agnosia, autism, and dyslexia. He earned his PhD in biomedical engineering and MD from the Johns Hopkins University in 1998, and did his postdoctoral training in primate visual neurophysiology at Baylor College of Medicine. He joined the MIT faculty in 2002.

Mehrdad Jazayeri, Robert A. Swanson (1969) Career Development Professor of Life Sciences

Mehrdad Jazayeri’s research explores the neural mechanisms that underlie time perception, deliberation, and probabilistic reasoning across scales, from individual neurons to neural circuits and behavior. His research employs multiple techniques including human psychophysics, computational modeling, and neurophysiology. His most recent work combines electrophysiology with optogenetics in order to analyze neural function in the primate brain. Jazayeri's current research is focused on the mechanisms that enable the brain to process information probabilistically on a flexible time scale, mental capacities that are crucial for cognitive functions such as anticipation, inferring causes, and sequencing thoughts and actions. Jazayeri joined the MIT faculty in January 2013 as an assistant professor in the Department of Brain and Cognitive Sciences and a McGovern Investigator. He obtained a BS in electrical engineering from Sharif University of Technology in Tehran, Iran, and a PhD from New York University, where he studied with J. Anthony Movshon. After graduating, he joined the laboratory of Michael Shadlen as a Helen Hay Whitney postdoc at the University of Washington.

Jonathan Kelner focuses on the application of techniques from pure mathematics to the solution of fundamental problems in algorithms and complexity theory. With the goal of developing practical algorithms for real-world questions, Kelner has made contributions in the areas of combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory, among others. After receiving a bachelor’s degree at Harvard University, he completed his doctoral work in computer science at MIT in 2006. Before joining the Department of Mathematics and the Computer Science and Artificial Intelligence Laboratory (CSAIL), he spent a year as a member of the Institute for Advanced Study.

Jared Speck, Cecil and Ida Green Career Development Professor

Jared Speck is an analyst of nonlinear partial differential equations that are used to model physical phenomena. In particular, he is interested in Einstein's equations, relativistic fluid mechanics, kinetic theory, and nonlinear electromagnetism. In his recent work, he has explored stable singularity formation. Examples include the formation of shocks in solutions to 3-D wave equations and the formation of Big Bang singularities in solutions to the Einstein equations. Speck received a PhD from Rutgers University in 2008, under the guidance of Michael Kiessling and Shadi Tahvildar-Zadeh. He completed a BS in mathematics from the University of Maryland in 2002. He served as lecturer for a year at Princeton University, before going to Cambridge University on a research appointment, working with Mihalis Dafermos. He returned to Princeton in 2010-11 as a National Science Foundation Fellow working with Igor Rodnianski and Sergiu Klainerman. He joined the MIT Department of Mathematics in 2011, and was awarded a Sloan Research Fellowship in 2014.